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    DISTRIBUTED LABELING FOR SUPERVISED LEARNING
    1.
    发明申请
    DISTRIBUTED LABELING FOR SUPERVISED LEARNING 审中-公开

    公开(公告)号：WO2020068360A1

    公开(公告)日：2020-04-02

    申请号：PCT/US2019/048924

    申请日：2019-08-29

    Applicant: APPLE INC.

    Inventor： BHOWMICK, Abhishek ,  ROGERS, Ryan M. ,  VAISHAMPAYAN, Umesh S. ,  VYRROS, Andrew H.

    IPC: G06N3/04

    Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY
    2.
    发明申请
    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY 审中-公开

    公开(公告)号：WO2019231627A2

    公开(公告)日：2019-12-05

    申请号：PCT/US2019/031319

    申请日：2019-05-08

    Applicant: APPLE INC.

    Inventor： BHOWMICK, Abhishek ,  ROGERS, Ryan M. ,  VAISHAMPAYAN, Umesh S. ,  VENKATRAMAN, Kartik R.

    IPC: G06F17/27 ,  G06N20/00

    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY
    3.
    发明申请
    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY 审中-公开

    公开(公告)号：WO2019231627A3

    公开(公告)日：2019-12-05

    申请号：PCT/US2019/031319

    申请日：2019-05-08

    Applicant: APPLE INC.

    Inventor： BHOWMICK, Abhishek ,  ROGERS, Ryan M. ,  VAISHAMPAYAN, Umesh S. ,  VENKATRAMAN, Kartik R.

    IPC: G06F17/27 ,  G06N20/00

    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

    PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS
    5.
    发明公开
    PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS 审中-公开

    公开(公告)号：EP3525388A3

    公开(公告)日：2019-11-27

    申请号：EP19153349.6

    申请日：2019-01-23

    Applicant: Apple Inc.

    Inventor： BHOWMICK, Abhishek ,  VYRROS, Andrew H. ,  ROGERS, Ryan M.

    IPC: H04L9/00 ,  G06N20/00

    Abstract: One embodiment provides for a mobile electronic device comprising a non-transitory machine-readable medium to store instructions, the instructions to cause the mobile electronic device to receive a set of labeled data from a server; receive a unit of data from the server, the unit of data of a same type of data as the set of labeled data; determine a proposed label for the unit of data via a machine learning model on the mobile electronic device, the machine learning model to determine the proposed label for the unit of data based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.

    PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS
    7.
    发明公开
    PRIVATIZED MACHINE LEARNING USING GENERATIVE ADVERSARIAL NETWORKS 审中-公开

    公开(公告)号：EP3525388A2

    公开(公告)日：2019-08-14

    申请号：EP19153349.6

    申请日：2019-01-23

    Applicant: Apple Inc.

    Inventor： BHOWMICK, Abhishek ,  VYRROS, Andrew H. ,  ROGERS, Ryan M.

    IPC: H04L9/00 ,  G06N20/00

    Abstract: One embodiment provides for a mobile electronic device comprising a non-transitory machine-readable medium to store instructions, the instructions to cause the mobile electronic device to receive a set of labeled data from a server; receive a unit of data from the server, the unit of data of a same type of data as the set of labeled data; determine a proposed label for the unit of data via a machine learning model on the mobile electronic device, the machine learning model to determine the proposed label for the unit of data based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.

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